Affective facial expression processing via simulation: A probabilistic model

Publication Type:
Journal Article
Biologically Inspired Cognitive Architectures, 2014, 10 (C), pp. 30 - 41
Issue Date:
Filename Description Size
published_paper.pdfPublished Version1.26 MB
Full metadata record
© 2014 Published by Elsevier B.V. Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in 'mind-reading' are complex. One explanation of such processes is Simulation Theory-it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity ('internal transcoding of external stimuli'), and then onto a latent space ('phenomenological response'). Together with the proposed architecture we present some promising preliminary results.
Please use this identifier to cite or link to this item: